Course Summary
This capstone project course on AI in Healthcare is designed to put your newly acquired knowledge and skills to the test. You will be working with real-world healthcare datasets to build and deploy AI models to solve healthcare problems.Key Learning Points
- Learn to apply AI and machine learning techniques to healthcare datasets
- Explore real-world healthcare problems and develop solutions using AI models
- Gain hands-on experience by working on a capstone project
Related Topics for further study
Learning Outcomes
- Ability to apply AI and machine learning techniques to healthcare datasets
- Experience solving real-world healthcare problems using AI models
- Proficiency in deploying AI models for healthcare applications
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming and statistics
- Familiarity with healthcare data
Course Difficulty Level
AdvancedCourse Format
- Online self-paced course
- Capstone project
Similar Courses
- AI for Medical Diagnosis
- AI for Medical Prognosis
Related Education Paths
- IBM Data Science Professional Certificate
- Deep Learning Specialization
- Applied Data Science with Python Specialization
Notable People in This Field
- Cardiologist and Digital Medicine Researcher
- Professor of Computer Science, Stanford University
Related Books
Description
This capstone project takes you on a guided tour exploring all the concepts we have covered in the different classes up till now. We have organized this experience around the journey of a patient who develops some respiratory symptoms and given the concerns around COVID19 seeks care with a primary care provider. We will follow the patient's journey from the lens of the data that are created at each encounter, which will bring us to a unique de-identified dataset created specially for this specialization. The data set spans EHR as well as image data and using this dataset, we will build models that enable risk-stratification decisions for our patient. We will review how the different choices you make -- such as those around feature construction, the data types to use, how the model evaluation is set up and how you handle the patient timeline -- affect the care that would be recommended by the model. During this exploration, we will also discuss the regulatory as well as ethical issues that come up as we attempt to use AI to help us make better care decisions for our patient. This course will be a hands-on experience in the day of a medical data miner.
Outline
- Getting Started, Phase 1: Data Collection
- Introduction
- Phase 1: Data Collection
- Phase 1. Project 1
- Phase 1. Project 2
- Phase 2: Model Training Part 1
- Phase 2: Model Training, Part 1
- Phase 2. Project 1
- Phase 2. Project 2
- Phase 3: Model Training Part 2
- Phase 3: Model Training, Part 2
- Phase 3. Project 1
- Phase 3. Project 2
- Phase 4: Model Evaluation
- Phase 4: Model Evaluation
- Phase 4. Project 1
- Phase 4. Project 2
- Phase 5: Model Deployment and Regulation, Wrap Up
- Phase 5: Model Deployment and Regulation
- Wrap Up
- Claim CME Credit
- Phase 5
Summary of User Reviews
The AI in Healthcare Capstone course on Coursera has received positive feedback from users. The course has been praised for its comprehensive coverage of AI in healthcare and its practical approach to learning. Many users have found the project-based assignments to be particularly helpful in gaining hands-on experience.Key Aspect Users Liked About This Course
Project-based assignmentsPros from User Reviews
- Comprehensive coverage of AI in healthcare
- Practical approach to learning
- Hands-on experience through project-based assignments
- Engaging and knowledgeable instructors
- Excellent support from the Coursera community
Cons from User Reviews
- Some technical concepts may be challenging for beginners
- Course may be time-consuming
- Some users found the pacing to be too slow
- Limited interaction with instructors
- Lack of feedback on assignments
Matthew LungrenTop Instructor
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